The objective of the present review is to discuss the results of published studies that show how nutrition affects the expression of genes involved in lipid metabolism and how diet manipulation might change marbling and composition of fat in beef. Several key points in the synthesis of fat in cattle take place at the molecular level, and the association of nutritional factors with the modulation of this metabolism is one of the recent targets of nutrigenomic research. Within this context, special attention has been paid to the study of nuclear receptors associated with fatty acid metabolism. Among the transcription factors involved in lipid metabolism, the peroxisome proliferator-activated receptors (PPARs) and sterol regulatory element-binding proteins (SREBPs) stand out. The mRNA synthesis of these transcription factors is regulated by nutrients, and their metabolic action might be potentiated by diet components and change lipogenesis in muscle. Among the options for dietary manipulation with the objective to modulate lipogenesis, the use of different sources of polyunsaturated fatty acids, starch concentrations, forage ratios and vitamins stand out. Therefore, special care must be exercised in feedlot feed management, mainly when the goal is to produce high marbling beef.

The present review will present the recent published results and discuss the main effects of nutrients, mainly fatty acids, on the expression of genes involved in lipid metabolism. In this sense, the review focuses in two phases: prenatal life and finishing phase, showing how nutrients can modulate gene expression affecting marbling and fatty acid profile in meat from ruminants. Adiposity in ruminants starts to be affected by nutrients during prenatal life when maternal nutrition affects the differentiation and proliferation of adipose cells enhancing the marbling potential. Therefore, several fetal programming studies were carried out in the last two decades in order to better understand how nutrients affect long-term expression of genes involved in adipogenesis and lipogenesis. In addition, during the finishing phase, marbling becomes largely dependent on starch digestion and glucose metabolism, being important to create alternatives to increase these metabolic processes, and modulates gene expression. Different lipid sources and their fatty acids may also influence the expression of genes responsible to encode enzymes involved in fat tissue deposition, influencing meat quality. In conclusion, the knowledge shows that gene expression is a metabolic factor affecting marbling and fatty acid profile in ruminant meat and diets and their nutrients have direct effect on how these genes are expressed.

Proteins are the major constituents of muscle and are key molecules regulating the metabolic changes during conversion of muscle to meat. Brazil is one of the largest exporters of beef and most Brazilian cattle are composed by zebu (Nellore) genotype. Bos indicus beef is generally leaner and tougher than Bos taurus such as Angus. The aim of this study was to compare the muscle proteomic and phosphoproteomic profile of Angus and Nellore. Seven animals of each breed previously subjected the same growth management were confined for 84 days. Proteins were extracted from Longissimus lumborum samples collected immediately after slaughter and separated by two-dimensional electrophoresis. Pro-Q Diamond stain was used in phosphoproteomics. Proteins identification was performed using matrix assisted laser desorption/ionization time-of-flight mass spectrometry. Tropomyosin alpha-1 chain, troponin-T, myosin light chain-1 fragment, cytoplasmic malate dehydrogenase, alpha-enolase and 78 kDa glucose-regulated protein were more abundant in Nellore, while myosin light chain 3, prohibitin, mitochondrial stress-70 protein and heat shock 70 kDa protein 6 were more abundant in Angus (P<0.05). Nellore had higher phosphorylation of myosin regulatory light chain-2, alpha actin-1, triosephosphate isomerase and 14-3-3 protein epsilon. However, Angus had greater phosphorylation of phosphoglucomutase-1 and troponin-T (P<0.05). Therefore, proteins involved in contraction and muscle organization, myofilaments expressed in fast or slow-twitch fibers and heat shock proteins localized in mitochondria or sarcoplasmic reticulum and involved in cell flux of calcium and apoptosis might be associated with differences in beef quality between Angus and Nellore. Furthermore, prohibitin appears to be a potential biomarker of intramuscular fat in cattle. Additionally, differences in phosphorylation of myofilaments and glycolytic enzymes could be involved with differences in muscle contraction force, susceptibility to calpain, apoptosis and postmortem glycolysis, which might also be related to differences in beef quality among Angus and Nellore.

The use of digital images could be a faster and cheaper alternative technique to assess BW, HCW, and body composition of beef cattle. The objective of this study was to develop equations to predict body and carcass weight and body fat content of young bulls using digital images obtained through a Microsoft Kinect device. Thirty-five bulls with an initial BW of 383 (±5.38) kg (20 Black Angus, 390 [±7.48] kg initial BW, and 15 Nellore, 377 [±8.66] kg initial BW) were used. The Kinect sensor, installed on the top of a cattle chute, was used to take infrared light–based depth videos, recorded before the slaughter. For each animal, a quality control was made, running and pausing the video at the moment that the animal was standing with its body and head in line. One frame from recorded videos was selected and used to analyze the following body measurements: chest width, thorax width, abdomen width, body length, dorsal height, and dorsal area. From these body measurements, 23 indexes were generated and tested as potential predictors. The BW and HCW were assessed with a digital scale, whereas empty body fat (EBF) was estimated through ground samples of all tissues. To better understand the relationship among the measurements, the correlations between final BW (488 [±10.4] kg), HCW (287 [±12.5] kg), EBF (14 [±0.610] % empty BW) content, body measurements (taken through digital images), and developed indexes were evaluated. The REG procedure was used to develop the regressions, and the important independent variables were identified using the options STEPWISE and Mallow's Cp in the SELECTION statement. Chest width was the trait most related to weights and the correlations between this measurement and BW and HCW were above 0.85. The analysis of linear regressions between observed and predicted values showed that all models pass through the origin and have a slope of unity (null hypothesis [H₀]: a = 0 and b = 1; P ≥ 0.993). The models to estimate BW and HCW of Angus and Nellore presented R² between 0.69 and 0.84 (P < 0.001), whereas R² from equations to estimate the EBF were lower (R² = 0.43–0.45; P ≤ 0.006). Index I5 [(chest width)² × body length], related to the animal volume, was significant in all models created to estimate BW and HCW, and it explained more than 70% of the variation. This study indicates that digital images taken through a Microsoft Kinect system have the potential to be used as a tool to estimate body and carcass weight of beef cattle.